Sensing Subjective Well-being from Social Media

Subjective Well-being(SWB), which refers to how people experience the quality of their lives, is of great use to public policy-makers as well as economic, sociological research, etc. Traditionally, the measurement of SWB relies on time-consuming and costly self-report questionnaires. Nowadays, people are motivated to share their experiences and feelings on social media, so we propose to sense SWB from the vast user generated data on social media. By utilizing 1785 users' social media data with SWB labels, we train machine learning models that are able to "sense" individual SWB from users' social media. Our model, which attains the state-by-art prediction accuracy, can then be used to identify SWB of large population of social media users in time with very low cost.Comment: 12 pages, 1 figures, 2 tables, 10th International Conference, AMT 2014, Warsaw, Poland, August 11-14, 2014. Proceeding

Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users

If people with high risk of suicide can be identified through social media like microblog, it is possible to implement an active intervention system to save their lives. Based on this motivation, the current study administered the Suicide Probability Scale(SPS) to 1041 weibo users at Sina Weibo, which is a leading microblog service provider in China. Two NLP (Natural Language Processing) methods, the Chinese edition of Linguistic Inquiry and Word Count (LIWC) lexicon and Latent Dirichlet Allocation (LDA), are used to extract linguistic features from the Sina Weibo data. We trained predicting models by machine learning algorithm based on these two types of features, to estimate suicide probability based on linguistic features. The experiment results indicate that LDA can find topics that relate to suicide probability, and improve the performance of prediction. Our study adds value in prediction of suicidal probability of social network users with their behaviors

Analysis of Users’ Sentiments in Social Media (on the Example of the Astrakhan Region)

The article is devoted to the studying of the opinions and sentiments of users of regional communities in the social network VKontakte using methods of machine analysis of text data, supplemented by sociological research methods. In the course of the study, we identified a list of current topics discussed by the inhabitants of the region, determined the most frequently mentioned persons, and analyzed the tone of their mention. Additionally, on the basis of the obtained results, the index of subjective (non-) well-being (ISW) was calculated for each district of the region and a map of the emotional coloring of posts from the communities of the analyzed social network was built. The results of the study can be used to monitor the situation in the region, finding problem areas, elicitation opinion leaders (popular personalities of the region that have a special influence on the opinion of the population), as well as identify the most interesting topics and urgent problems for the population. In perspective, this method of monitoring the social sentiments of the population of the region can be improved by automating the addition of new data to the analytical project. In the future, the addition of mathematical models to the system will make it possible to create graphs for predicting further changes in the region

Classification community publications of the "VKontakte" for assessing the quality of life of the population

Сегодня социальные сети — это повседневный инструмент пользователя для выражения своих мнений и предпочтений. Цифровые следы, создаваемые в сети, являются ценным источником данных для выделения проблем населения в различных сферах жизнедеятельности. Фокус данной работы сосредоточен на разработке алгоритма, позволяющего автоматически классифицировать текстовый контент социальной сети «ВКонтакте», являющейся одной из популярных платформ среди пользователей, по категориям качества жизни: «образование», «здравоохранение», «безопасность», «социальное обеспечение», «работа органов власти», «экология» и «доступность товаров и услуг». Для реализации поставленной задачи в рамках работы использованы статичные и контекстуализированные модели создания векторных представлений и эффективные алгоритмы классификации русскоязычного контента социальных сетей (LSTM, BiLSTM, GRU, RuBERT). На сегодняшний день мы отдаем предпочтение модели RuBERT-tiny за счет лучших показателей полноты в большинстве категорий

Subjective measurement of population ill-being/well-being in the Russian regions based on social media data

В статье обосновывается новый метод субъективной оценки благополучия, основанный на анализе онлайн-активности пользователей в социальных сетях. Предлагаемый метод обладает как преимуществами (быстрота исследования, небольшие затраты, масштабность, детальность полученной информации), так и определенными ограничениями (охват «цифрового населения», технические сложности при исследовании густонаселенных мегаполисов и т. д.). В статье представлены результаты эмпирического исследования онлайн-активности пользователей социальной сети «ВКонтакте». На основе полученных данных рассчитан индекс субъективного (не)благополучия для 43 регионов РФ по 19 показателям, охватывающих экономические, социальные и политические аспекты качества жизни людей. Индекс субъективного (неблагополучия строится на изучении онлайн-активности пользователей, входящих в 1350 наиболее популярных региональных и городских сообществ в социальной сети «ВКонтакте». Период исследования включает в себя весь 2018 год

Social media mental health analysis framework through applied computational approaches

Studies have shown that mental illness burdens not only public health and productivity but also established market economies throughout the world. However, mental disorders are difficult to diagnose and monitor through traditional methods, which heavily rely on interviews, questionnaires and surveys, resulting in high under-diagnosis and under-treatment rates. The increasing use of online social media, such as Facebook and Twitter, is now a common part of people’s everyday life. The continuous and real-time user-generated content often reflects feelings, opinions, social status and behaviours of individuals, creating an unprecedented wealth of person-specific information. With advances in data science, social media has already been increasingly employed in population health monitoring and more recently mental health applications to understand mental disorders as well as to develop online screening and intervention tools. However, existing research efforts are still in their infancy, primarily aimed at highlighting the potential of employing social media in mental health research. The majority of work is developed on ad hoc datasets and lacks a systematic research pipeline. [Continues.]</div